Genetic Algorithms Made Easy
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Genetic Algorithms Made Easy

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This is an easy introduction to the concept of Genetic Algorithms. It gives Simple explanation of Genetic Algorithms. Covers the major steps that are required to implement the GA for your tasks.......

This is an easy introduction to the concept of Genetic Algorithms. It gives Simple explanation of Genetic Algorithms. Covers the major steps that are required to implement the GA for your tasks.
For other resources visit: http://pimpalepatil.googlepages.com/
For more information mail me on pbpimpale@gmail.com

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  • 1. A Seminar on Visit: http://pimpalepatil.googlepages.co m/ Genetic Algorithm by Prakash B. Pimpale & Nitin S. Bhande & Guided by: prof. Suhas S. Gajare Department of electronics & telecom. Engg. SGGS Institute of engg. & technology, Nanded. Thursday, July 02, Prakash B. Pimpale & Nitin S. 1 2009 Bhande
  • 2. Contents: • Introduction • History • Biological background • GAs • Example • Applications • Advantages • Limitations • Future scope • Conclusion • References B. Pimpale & Nitin S. Thursday, July 02, Prakash 2 2009 Bhande
  • 3. Introduction: • Genetic algorithms are a part of evolutionary computing, which is a rapidly growing area of artificial intelligence. • Genetic algorithms (GA’s) are a technique to solve problems which need optimization • Based on idea that evolution represents search for optimum solution set • GA’s are based on Darwin’s theory of evolution Thursday, July 02, Prakash B. Pimpale & Nitin S. 3 2009 Bhande
  • 4. History: • Evolutionary computing evolved in the 1960’s by I. Rechenberg in his work "Evolution strategies" • GA’s were invented by John Holland in the mid-70’s. • Jhon Holland wrote "Adaption in Natural and Artificial Systems" which was published in 1975. • In 1992 John Koza used GA’s for Genetic ProgrammingPimpale & Nitin S. Thursday, July 02, Prakash B. (GP) 4 2009 Bhande
  • 5. Biological background: Thursday, July 02, Prakash B. Pimpale & Nitin S. 5 2009 Bhande
  • 6. Genetic algorithms: • Search space • Basic algorithm • Encoding • Crossover & Mutation • Selection on the basis of fitness Thursday, July 02, Prakash B. Pimpale & Nitin S. 6 2009 Bhande
  • 7. Search space: -“The set of solutions among which the desired solution resides”. -With GA we look for the best solution among among a number of possible solutions - represented by one point in the search space Possible solutions Search space Thursday, July 02, Prakash B. Pimpale & Nitin S. 7 2009 Bhande
  • 8. Basic algorithm: 0 START : Create random population of n chromosomes 1 FITNESS : Evaluate fitness f(x) of each chromosome in the population 2 NEW POPULATION: 0 SELECTION : Based on f(x) 1 RECOMBINATION : Cross-over chromosomes 2 MUTATION : Mutate chromosomes 3 ACCEPTATION : Reject or accept new one 3 REPLACE : Replace old with new population: the new generation 4 TEST : Test problem criterium 5 LOOP : Continue step 1 – 4 until criterium is satisfied Thursday, July 02, Prakash B. Pimpale & Nitin S. 8 2009 Bhande
  • 9. Pictorial basic algo. Thursday, July 02, Prakash B. Pimpale & Nitin S. 9 2009 Bhande
  • 10. Encoding: -“Coding of the population for evolution process”. *Binary Encoding: In binary encoding, every chromosome is a string of bits - 0 or 1. Chromosome A 101100101100101011100101 Chromosome B 111111100000110000011111 *Permutation Encoding: In permutation encoding, every chromosome is a string of numbers that Chromosome A 1 5 3 2 6 4 7 9 8 represent a position in a sequence. Chromosome B 8 5 6 7 2 3 1 4 9 Thursday, July 02, Prakash B. Pimpale & Nitin S. 10 2009 Bhande
  • 11. Encoding continued- *Value Encoding: Values can be anything connected to problem. Chromosome A 1.2324 5.3243 0.4556 2.3293 2.4545 Chromosome B ABDJEIFJDHDIERJFDLDFLFEGT Chromosome (back), (back), (right), (forward), (left) C *Tree Encoding: In this every chromosome is tree of some function or command in prog. language Chromosome A Chromosome B Thursday, July 02, ( + Prakash )B. Pimpale & Nitin S.( do_until step wall ) x (/ 5 y ) 11 2009 Bhande
  • 12. Crossover : 1.Single point crossover : 11001011+11011111 = 11001111 2.Two point crossover : 11001011 + 11011111 = 11011111 3.Uniform crossover: 11001011 + 11011101 = 11011111 Thursday, July 02, Prakash B. Pimpale & Nitin S. 12 2009 Bhande
  • 13. Mutation: #Bit inversion: Selected bits are inverted 11001001 => 10001001 #Order changing: Two numbers are selected and exchanged (1 2 3 4 5 6 8 9 7) => (1 8 3 4 5 6 2 9 7) Thursday, July 02, Prakash B. Pimpale & Nitin S. 13 2009 Bhande
  • 14. Selection on the basis of fitness: 1.Rank selection: Ranks the population first and then every chromosome receives fitness value determined by this ranking. The worst will have the fitness 1, the second worst 2 etc. and the best will have fitness N (number of chromosomes in population). Situation before ranking (graph of fitnesses) Situation after ranking (graph of order numbers) Thursday, July 02, Prakash B. Pimpale & Nitin S. 14 2009 Bhande
  • 15. 2. Roulette Wheel Selection: -The size of the section in the roulete wheel is proportional to the value of the fitness function of every chromosome - the bigger the value is, the larger the section is. -A marble is thrown in the roulette wheel and the chromosome where it stops is selected. Thursday, July 02, Prakash B. Pimpale & Nitin S. 15 2009 Bhande
  • 16. 3.Steady-State Selection: -Allows big part of chromosomes to survive to next generation. -few good (with higher fitness) chromosomes are selected for creating new offspring -Then some bad (with lower fitness) chromosomes are removed and the new offspring is placed in their place -Thus process continues until we get optimal solution Thursday, July 02, Prakash B. Pimpale & Nitin S. 16 2009 Bhande
  • 17. 4.Elitism: - The best chromosomes are copied first & then the evolution proceeds - This prevents loss of the best found solution - Elitism can rapidly increase the performance of GA Thursday, July 02, Prakash B. Pimpale & Nitin S. 17 2009 Bhande
  • 18. Example: 120 100 80 60 y 40 20 0 0 10 20 30 40 50 60 70 80 90 100 x Graph of cities Thursday, July 02, Prakash B. Pimpale & Nitin S. 18 2009 Bhande
  • 19. Coding for eight city problem: Encoding used Permutation Encoding- 1) Aurangabad 2) Jalana 3) Parbhani 4) Manvat 5) Nanded 6) Latur 7) Hingoli 8) Selu CityList1 (1 2 8 7 5 6 4 3) CityList2 (2 8 7 1 3 4 5 6) Thursday, July 02, Prakash B. Pimpale & Nitin S. 19 2009 Bhande
  • 20. Crossover- Parent1 (1 2 8 7 5 6 4 3) parent2 (2 8 7 1 3 4 5 6) Child (1 2 8 7 3 4 5 6) Thursday, July 02, Prakash B. Pimpale & Nitin S. 20 2009 Bhande
  • 21. Mutation- Mutation involves reordering of the list: * * Before: (1 2 8 7 3 4 5 6) After: (1 2 8 4 3 7 5 6) ->this is what the shortest path covering all cities! -so solution is as (1 2 8 4 3 7 5 6) Thursday, July 02, Prakash B. Pimpale & Nitin S. 21 2009 Bhande
  • 22. Applications: -Designing neural networks, both architecture and weights -In Strategy planning -TSP and sequence scheduling -In game playing -Stock market predictions -In mathematics -And many more Thursday, July 02, Prakash B. Pimpale & Nitin S. 22 2009 Bhande
  • 23. Advantages: • Concept is easy to understand • genetic algorithms are intrinsically parallel. • Always an answer; answer gets better with time • Inherently parallel; easily distributed • Less time required for some special applications • Chances of getting optimal solution are more » and many more Thursday, July 02, Prakash B. Pimpale & Nitin S. 23 2009 Bhande
  • 24. Limitations: • The population considered for the evolution should be moderate or suitable one for the problem (normally 20-30 or 50-100) • Crossover rate should be 80%-95% • Mutation rate should be low i.e. 0.5%-1% assumed as best • The method of selection should be appropriate • Writing of fitness function must be accurate Thursday, July 02, Prakash B. Pimpale & Nitin S. 24 2009 Bhande
  • 25. Future scope: • For finding new crossover methods for more convergence in single step • For finding new mutation methods for reducing divergence due to excess mutation • For finding a function which will decide suitable number of initial population Thursday, July 02, Prakash B. Pimpale & Nitin S. 25 2009 Bhande
  • 26. Conclusion: -Genetic algorithms are rich - rich in application across a large and growing number of disciplines. -Really genetic algorithm changes the way we do computer programming. Thursday, July 02, Prakash B. Pimpale & Nitin S. 26 2009 Bhande
  • 27. References: 1)http://cs.felk.cvut.cz/~xobitko/ga/main.html 2)Genetic Algorithms and Evolutionary Computation by Adam Marczyk 3)official site ofIllinois Genetic Algorithms Laboratory : http://www-illigal.ge.uiuc.edu/index.php3 4) Paper on Recentdevelopment in evolutionary & genetic algorithm: theory & applications. by N.Chaiyaratana & A.M.S.Zalzala 5)www.gwnetic~programming.com Thursday, July 02, Prakash B. Pimpale & Nitin S. 27 2009 Bhande
  • 28. Thursday, July 02, Prakash B. Pimpale & Nitin S. 28 2009 Bhande